Improved Adaptive Filter with Unknown Process and Measurement Noise Covariance

Abstract

This paper considers the problem of state estimation with unknown process and measurement noise covariance for a linear Gaussian system. Under the assumption of conjugate priors, inverse Wishart distribution is chosen for both process and measurement noise covariance by introducing a latent variable. Then, the joint state estimation and unknown parameters identification is derived in variational Bayesian framework, and the system state, latent variable, process and measurement noise covariance are updated iteratively. The performance of the proposed algorithm is demonstrated by comparing with the other VB based adaptive filter in a target tracking simulation system.